Designing a New Class of Distributed Systems (SpringerBriefs in Electrical and Computer Engineering)

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Would you like us to take another look at this review? No, cancel Yes, report it Thanks! For example, if two computing nodes involved in collaboration with each other are in two different devices, the communication channel must be switched to socket communication, where as if they are communicating across two cores, shared memory would be an appropriate resource to be allocated.

In a web based distributed transaction that spans across multiple geographies, the dynamic nature of the transaction demands dynamic resource allocation to optimally 26 2 Understanding Distributed Systems Fig. Current operating systems and management systems fall short in providing dynamic resiliency, efficiency and scaling for two reasons: It is clear that current approaches to resource management, albeit with automation, are not sensitive to the distributed nature of transactions and contention References 27 Fig. Comparing the computing machines and living organisms, he points out that the computing machines are not as fault tolerant as the living organisms.

More recent efforts, in a similar vein, are looking at resiliency borrowing from biological principles [21] to design future Internet architecture. In the next chapter, we will revisit the design of distributed systems with a new non-von Neumann computing model called Distributed Intelligent Managed Element DIME1 Network computing model that integrates computational workflows with a parallel implementation of management workflows to provide dynamic real-time FCAPS management of distributed services and end-to-end service transaction management.

The DIME network architecture provides a new direction to harness the power of many core servers with the architectural resiliency of cellular organisms and a high degree of scaling and efficiency. Birman, Reliable Distributed Systems: Kindberg, Distributed Systems, Concepts and Design, 3rd edn. Addison Wesley, New York, , p.

Monod, Genetic regulatory mechanisms in the synthesis of proteins. Sako, , http: Accessed 27 November from Asia Research News: Malone, Organizing Information Processing Systems: Black Ablex Publishing, New Jersey, Accessed 12 February Brandicc, Cloud computing and emerging IT platforms: Agarwal, Factored operating systems fos: Infrastructures for Collaborative Enterprises, , pp. Infrastructures for Collaborative Enterprises, Each node is a computing entity a Turing machine implemented using von-Neumann computing model modified by endowing it with self-management and signaling capabilities to collaborate with similar nodes in a network.

The separation of parallel computing and management channels allows the end to end transaction management of computing tasks provided by the autonomous distributed computing elements to be implemented as network-level FCAPS management. However, the model lends itself to be implemented i from scratch to exploit the many core servers and ii in current generation servers exploiting multi-thread computing features available in current operating systems such as Linux and Windows. For a description of the DIME network architecture and the genetic transactions, please see the video http: The computing node is either a core in a many-core server, a process in a conventional operating system, or a processor in any mobile device or a laptop.

Multiple threads available in each core or an operating system process implementation are exploited to implement a self-managed computing element called the DIME. Each DIME presents a computing element that can execute a managed computing process with fault, configuration, accounting, performance and security management. The DIME network computing model exploits the multithread capability offered in the computing element either as a process in a conventional operating system or as a core in a many-core system to separate management and computing threads.

The parallelism is exploited to implement the management of a Turing machine. The parallel signaling network allows the management of a network of managed Turing nodes. The recursive network composition model is ideally suited to implement recursive state machines and thus implement service workflows.

The DIME Network Architecture and the Anatomy of a DIME 31 The parallelism of service execution and service regulation allows real-time monitoring of service behavior and control, based on policies and constraints specified by the regulators both at the node level and at the network level. The signaling control network allows parallel management of the service workflow. There are three key features in this model that differentiate it from all other models: The self-management features of each SPC node with FCAPS management using parallel threads allow autonomy in controlling local resources and provide services based on local policies.

Each node keeps its state information and history of its transactions. These features provide the powerful genetic transactions namely, replication, repair, recombination and reconfiguration that have proven to be essential for the resiliency of cellular organisms [3]. The description contains the resources required, the constraints and the addresses of executable modules for various components and various run time commands the DIME obeys.

Signaling allows groups of DIMEs to collaborate with each other and implement global policies with high degree of agility that the parallelism offers. The signaling abstractions are: Each DIME is capable of self-identification, heartbeat broadcast, and provides a published alerting interface that describes various alerting attributes and its own FCAPS management. Each DIME is a member of a network with a purpose and a role. Supervision allows contention resolution based on roles and purpose. Supervision also allows policy monitoring and control.

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When the DIMEs are contending for resources to accomplish their specific mission, or require prioritization of their activities, the supervision hierarchy is assisted with mediation object network that provides global policy enforcement. It sends or receives commands related to the management and setting up of DIME to guarantee a scalable, secure, robust and reliable workflow execution. It also provides inter-DIME switching and routing1 functions.

This enables both the ability to set up the execution environment on the basis of the user requirements and, overall, the ability to reconfigure this environment, at run-time, in order to adapt it to new, foreseen or unforeseen, conditions e. It processes the events received from the SM or from the MM and configures the MICE appropriately to load and execute specific tasks by loading program and data modules available from specified locations. This makes the FM a key component of the entire system: It handles autonomously all the issues regarding the management of faults, resources utilization, performance monitoring and security, 2.

It simplifies the configuration of several environments on the same DIME to provide appropriate FCAPS management of each task that is assigned to the MICE which in turn, performs the processing of the task based on an associated profile. Not all the components seen above have to be active, at the same time: A single node of a DIME can execute a workflow by itself.

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Instantiating a subnetwork provides a way to implement a managed DAG executing a workflow. Replication is implemented by executing the same service as shown in Fig. Note that S1 is a service that can be programmed to terminate instantiating itself further when resources are not available. In addition, dynamic FCAPS parallel service monitoring and control management allows changing the behavior of any instance from outside using the signaling infrastructure to alter the service that is executed. The ability to execute the control commands in parallel allows dynamic reconfiguration or replacement of services during run time.

The workflow orchestrator instantiates the worker nodes, monitors heartbeat and performance of workers and implement fault tolerance, recovery, and performance management policies. DIME Network Architecture with a Native OS in a Multi-Core Processor Current generation Operating systems cannot scale to encompass the resource management when the number of cores in a many core server reaches a threshold dictated by mechanisms choosing correct lock granularity for performance, reasoning about correctness, and deadlock prevention.

The impact of the operating system gap the difference between the number of cores available in a server and the number of cores visible to a single instance of the operating system deployed in it is dramatic when you consider current deployment scenarios.

In one instance, a core server is used as dual core servers with Linux images. It is implemented to execute on bit multi-core Intel processors. Security is provided at the hardware level for this memory protection. Once a program has completed its execution, all memory that it had in use is returned to the system. Limits can be set on how much memory each DIME is able to allocate for itself. Memory is divided into a shared memory partition where the Parallax Kernel resides and partitions that are devoted to each core.

Memory can be dynamically adjusted on each core on as needed basis in 2 MiB chunks.

With dedicated resources, each DIME can be viewed as its own separate computing entity. If a DIME completes its task and is free, it is given back to the pool of available resources. The network management assures discovery and allocation of available DIMEs in the pool for new tasks. The signaling allows addressability at the thread level. Parallax offers local storage per server Shared with each DIME within the system as well as centralized file storage shared via the Orchestrator between all servers. Booting the OS via the network is also a possibility for systems that do not need permanent storage or for cost saving measures.

Under Parallax, all network communication is done over raw Ethernet frames. By using raw packets we have created a much simpler communication framework as well as removed the overhead of higher-level protocols, thereby increasing the maximum throughput. The use of raw Ethernet packets has already seen great success with the ATAoE protocol invented by Co-Raid for use in their network storage devices.

With the signaling layer, program parameters can be adjusted during run-time. The Orchestrator, from which the policies are implemented, communicates with the DIMEs for the purpose of coordination and control. Instruction types can be directly encoded into the bit Ether-Type field of each Ethernet frame shown in Fig. By making use of the EtherType field for specific purposes we can streamline the way in which packets are routed within a system.

The proof-of-concept prototype system consists of three components: It enables fault management by broadcasting a heartbeat over the signaling network. It allows loading, executing, and stopping an executable on demand. It supports DIME discovery through signaling channel. A run-time service orchestrator that allows DIME network management.

There are parallel efforts that are underway to architect a new OS for many-core servers: It is predicated on two central ideas: STP provides performance isolation and strong partitioning of resources among interacting software components, called Cells. Two-Level Scheduling separates global decisions about the allocation of resources to Cells from application-specific scheduling of resources within Cells.

It uses a multi-kernel model which calls for multiple independent OS instances communicating via explicit messages. Barrelfish factors the OS instance on each core into a privileged-mode CPU driver and a distinguished user-mode monitor process. CPU drivers are purely local to a core, and all inter-core coordination is performed by monitors. The distributed system of monitors and their associated CPU drivers encapsulate the functionality found in a typical monolithic microkernel such as scheduling, communication, and low-level resource allocation.

The rest of Barrelfish consists of device drivers and system services such as network stacks, memory allocators, etc. A video of the demo is available at http: We describe FOS which is built in a message passing manner, out of a collection of Internet inspired services. Each operating system service is factored into a set of communicating servers which in aggregate implement a system service.

These servers are designed much in the way that distributed Internet services are designed, but instead of providing high level Internet services, these servers provide traditional kernel services and replace traditional kernel data structures in a factored, spatially distributed manner. FOS replaces time sharing with space sharing. Helios is an operating system designed to simplify the task of writing, deploying, and tuning applications for heterogeneous platforms. Helios introduces satellite kernels, which export a single, uniform set of OS abstractions across CPUs of disparate architectures and performance characteristics.

Helios retargets applications to available ISAs by compiling from an intermediate language. Barrelfish focuses on gaining a fine-grained understanding of application requirements when running applications, while the focus of Helios is to export a single-kernel image across heterogeneous coprocessors to make it easy for applications to take advantage of new hardware platforms. The authors argue that applications should control sharing: Guided by this design principle, this chapter proposes three operating system abstractions address ranges, kernel cores, and shares that allow applications to control inter-core sharing and to take advantage of the likely abundance of cores by dedicating cores to specific operating system functions.

Measurements of micro-benchmarks on the Corey prototype operating system, which embodies the new abstractions, show how control over sharing can improve performance. Application benchmarks, using MapReduce and a Web server, show that the improvements can be significant for overall performance: Hardware event counters confirm that these improvements are due to avoiding operations that are expensive on multicore machines. All these approaches implement application services and the resource mediation services using the same serial von Neumann SPC model.

However, the DIME approach proposed in this chapter takes a different route to leverage the parallelism offered by multi-core and many-core architecture to implement the service management workflow as an overlay over the service workflow implemented over a network of SPC nodes. The separation and parallel implementation of the service regulation improve both the resilience, and the efficiency. The recursive or fractal-like network composition model eliminates the scaling limitation.

One implementation [4] uses the multi-process, multi-thread support in the Linux operating system to implement the DIME network. By encapsulating a Linux based processes with parallel FCAPS management and providing a parallel signaling channel, this implementation demonstrates auto-scaling, self-repair, live-migration, performance management and dynamic reconfiguration of workflows without the need for a Hypervisor-based server virtualization. These constraints allow the control of FCAPS management both at the node level and the sub-network level.

In essence, at each level in the DAG, the tuple gives the blueprint for both management and execution of the downstream graph. Under these considerations, it is easy to understand the power of the proposed solution in designing self-configuring, self-monitoring, selfprotecting, self-healing and self-optimizing distributed service networks. Signaling DIMEs responsible for the management layer at the network level are of the type Supervisor and the Mediator. The Supervisor sets up and controls the functioning of the sub network of DIMEs where the workflow is executed.

A Mediator is a specialized DIME for providing predefined roles such as fault or configuration or accounting or performance or security management. The deployment of DIMEs in the network, the number of signaling DIMEs involved in the management level, the number of available worker DIMEs and the division of the roles are established on the basis of the number and the type of tasks constituting the workflow and, overall, on the basis of the management profiles related to each task.

The profiles play a fundamental role in the proposed solution; each profile, in fact, contains the indication about the control and the configuration of both the signaling layer and execution environment for setting up the DIME that will handle the related task. The architectural innovation introduced here based on FCAPS and signaling abstractions radically transforms the Linux process implementation with a resiliency that surpasses current state of the art.

For example, fault management, performance management, security management are implemented at both the process level using a self-managed DIME and at the DIME network level which assures service workflow FCAPS management that spans across multiple processes that are distributed. When two DIMEs reside in the same enclosure where shared memory is more effective, the communication is dynamically configured to support shared memory.

In the many-core server, multiple images of Linux are deployed. In the next chapter, we will discuss various features demonstrated by the prototype. The programmability and execution of management at the node level and at the network level using parallel signaling network provides a fine-grain end-to-end distributed transaction management. It is therefore possible to implement end-toend resource management that contributes to a distributed transaction which 44 3 DIME Network Architecture Implementing Fig.

Constant monitoring and control based on required service level assurance brings the resiliency of cellular organisms to distributed transaction management. The separation of services management from underlying infrastructure management reduces or eliminates the dependence of applications on myriad server, network and storage management systems. As the number of cores in a many-core server increase, while current OSs and various management systems server management, virtual server management, network resource mediation systems and storage resource mediation systems increase the complexity, the scaling of DIME network architecture allows many of the features offered by current virtualization technologies such as auto-scaling, self-repair, auto-performance management, live migration etc.

Both signaling and service component network management allow a new way using service switching to provide FCAPS management which, heretofore has been provided by multiple resource management systems. The service-centricity as opposed to resource-centric management could offer simplicity of resource deployment with many-core server. When using a or core server and using WAN connectivity between the servers, it would be unnecessary to use Storage Area Networks with Fibre channel inside the server or the data center.

Similarly, with end-to-end transaction security management which controls reads and writes at every node, current Firewall and routing technologies need not be replicated inside the server. The signaling and FCAPS management at both the node level and the network level allows a simplification of service management by eliminating many of the current generation resource management automation systems and replacing it with services switching number of service transactions that are supported with FCAPS management in a distributed set of enclosures.

Each service transaction can be dynamically configured with assurance of FCAPS management of all the nodes that contribute to the transaction based on business priorities, workload fluctuations and latency constraints. In a network-centric service switching architecture an end-to-end distributed transaction becomes a connection management task. For example all reads and writes are controlled by network level and node level policies.

The service switching architecture brings features such as call waiting, call forwarding, call broadcast, and the service call to manage the distributed transaction FCAPS service levels based on service profiles of both suppliers and consumers. It is also important to note that while the hardware upheaval offers major cost savings in power and space savings alone, a transition from conventional computing with multi-tenancy at the physical server is improved by the multi-tenancy at the virtual server level by the number of virtual servers that can be run in a single enclosure.

The DIME network architecture takes the scaling to the next higher level by the number of service transactions that can be supported in an enclosure when more resources have to be added. In the next chapter we will discuss some applications of the DIME network architecture both in the short run where current generation hardware and software are transparently migrated and in the long run where a new class of distributed services are designed, deployed and assured using the new architecture. With a unifying paradigm, the DNA allows transparency of private and public clouds without any dependence on how the underlying infrastructure is deployed or managed as long as it supports a multi-threaded parallel execution of computing tasks.

Mikkilineni, Is the network-centric computing paradigm for multicore, the next big thing? Retrieved July 22 , from convergence of distributed clouds, grids and their management: Neumann, Theory of natural and artificial automata. Charles Babbage institute reprint series for the history of computing, vol. Moore, in Embryos, Genes and Birth Defects, 2nd edn. Moore John Wiley, London, , p. Agarwal, Factored operating systems FOS: DIMEs in Linux approach demonstrates the encapsulation of a Linux process as a DIME to demonstrate dynamic reconfiguration of service regulation to implement self-repair, autoscaling, performance management etc.

A native operating system called Parallax encapsulates each core into a DIME in a many-core server to demonstrate the implementation of a distributed service workflow with dynamic FCAPS management of distributed transactions.

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This chapter discusses how these prototypes could influence the next generation distributed services creation, delivery, and assurance infrastructure. Originally, the dial-tone was introduced to assure the telephone user that the exchange is functioning when the telephone is taken off-hook by breaking the silence before an operator responded with an audible tone. Later on, the automated exchanges provided a benchmark for telecom grade trust that assures managed resources on-demand with high availability, performance and security.

Today, as soon as the user goes on hook, the network recognizes the profile based on the dialing telephone number. As soon as the dialed party number is dialed, R. The continuous visibility and control of the connection allows service assurance even in the case of an earthquake or any such natural disaster. The reference model [1] shown in Fig. The reference model describes the relationships of various stakeholders 1 Infrastructure Providers, 2 Service Providers, 3 Service Developers, and 4 End Users.

Below, we revisit how the reference model will affect, benefit and be deployed by each of the stake holders. These are vendors who provide the underlying computing, network and storage infrastructure that can be carved up into logical clouds of computers which will be dynamically controlled to deliver massively scalable and globally interoperable service network infrastructure. The infrastructure will be used by both service creators who develop the services and also the end users who utilize these services.

This is very similar to switching, transmission and access equipment vendors in the telecom world who incorporate service enabling features and management interfaces right in their equipment. Current storage and computing server infrastructure has neither the ability to The Dial-Tone Metaphor and the Service Creation, Delivery and Assurance Platforms 49 dynamically dial-up and dial-down resources nor the capability for dynamic usageaware management which will help eliminate the numerous layers of present day management systems contributing to the total cost and human latency involved.

The new reference architecture provides requirements for the infrastructure vendors to eliminate current systems administration oriented management paradigm and enable next generation real-time, on-demand, FCAPS-based management so that applications can dynamically request the dial-up and dial-down of allocated resources. With the deployment of the infrastructure satisfying the requirements of the new reference architecture, service providers will be able to assure both service developers and service users that resources will be available on demand.

They will be able to effectively measure and meter resource utilization end-to-end usage to enable a dial-tone for computing service while managing service levels to meet the availability, performance and security requirements for each service. This is different from most current cloud computing solutions that are nothing more than hosted infrastructure or applications accessed over the Internet.

This will also enable a new distributed virtual services operating system that provides distributed FCAPS-based resource management on demand. They will be able to develop cloud-based services using the management services API to configure, monitor and manage service resource allocation, availability, utilization, performance and security of their applications in real-time. Service management and service delivery will now be integrated into application development to allow application developers to be able to specify run time service level agreements.

Their demand for choice, mobility and interactivity with intuitive user interfaces will continue to grow. The managed resources in the reference architecture will now not only allow the service developers to create and deliver services that end users can dynamically access on devices of their choice, but also enable service providers with the capability to provision in real-time to respond to changing demands, and to charge the end-users by metering exact resource usage for the desired service levels. Even with extensions to the von-Neumann computing model, such as cache memory, virtual memory and multi-threading, the service and its regulation are specified at compile time, executed serially and management cannot be controlled at run time.

Over time, 50 4 Designing Distributed Services Creation, Service Delivery and Service Assurance the static nature of service control which originated from the server-centric administrative paradigm is compensated by myriad administrative systems, specialized hardware solutions and cross-domain management systems resulting in the increase of both cost and complexity. The DIME network architecture on the other hand, exploits the parallelism to address the temporal phenomena involved in assuring transaction integrity in a distributed system.

Louise Barrett [2] making a case for the animal and human dependence on their bodies and environment—not just their brains—to behave intelligently, highlights the difference between Turing Machines implemented using von Neumann architecture and biological systems. GOFAI has had its successes, these have been somewhat limited, at least from our perspective here as students of cognitive evolution. This emphasis on the sensory monitoring of the environment, dynamic coupling, connectivity and system-wide coordination is also confirmed by observations on cell communication.

As mentioned in Chap. Cellular organisms developed very sophisticated computing models well before their brains evolved. The architectural resiliency of cellular organisms stems from their ability to manage highly temporal phenomena. System-wide connectivity and coordination require a sense of time, history and synchronization between various tasks performed by a group of loosely coupled elements which, as Louis Barrett points out, the Turing machine implemented using the stored program control lacks.

This could be the way that the underlying physical processes of the brain work how long it takes for a neurotransmitter, like nitric oxide or glutamate, to diffuse through the brain, for example, or how long it takes for such neurotransmitters to modulate neuronal activity , which in turn could affect the specific duration or rates of change in other DIME Network Architecture 51 Fig. A video explains the non-von Neumann behavior with parallel signaling overlay over the serial von-Neumann computing network http: Similar intrinsic rhythms in the body may also be important, as will other aspects of the body dynamics that relate to, for example, the mechanical properties of the muscle, which dictate where and how fast an animal can move.

These bodily processes may, in turn, need to be synchronized precisely with temporal processes occurring outside of the animal in the environment. Compare this with the quest for real-time information processing currently being driven by global communication, collaboration and commerce at the speed of light. Whether it is high frequency trading, web-based commerce, social networking or federated enterprise computing, the ability to manage highly temporal phenomena in real-time is becoming critical.

System-wide connectivity, high availability, security and performance management require coordination with a sense of time, history and synchronization between various tasks performed by a group of loosely coupled elements. The ability of the DIME network to monitor and control the service through the parallelization of service delivery and its regulation decouples the services management from the underlying hardware infrastructure management.

For example, if hardware that supports a particular DIME fails, the fault management policies monitoring the service heartbeat will immediately kick-in the 52 4 Designing Distributed Services Creation, Service Delivery and Service Assurance Fig. The services deployed either in the DIME node or a sub-network of DIMEs, which are affected by the hardware, are appropriately recovered based on the policies independent of the operating system or the hardware configuration of the hardware host.

This is in contrast to current cloud architecture where the services are not independent of the local operating system in this case a virtual server and the server configuration.

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The decoupling of services management from the underlying hardware infrastructure management allows designing and deploying highly reliable services without requiring highly reliable clusters and specialized enterprise class hardware. The resulting simplification and commoditization of infrastructure hardware hopefully, reduces costs of transactions and improves resiliency of service delivery.

Each Linux process is encapsulated in a DIME in which the service regulation and service execution are implemented in parallel. The Configuration Manager performs network-level configuration management and provides directory services. These services include registration, indexing, discovery, address management and communication management with other DIME networks.

The Performance Manager coordinates performance management at the network level and coordinates the performance using the information received through the signaling channel from each node. These constraints allow the control of FCAPS management both at the node level, the sub-network level, and the network level. The supervisor DIME, upon receiving the workflow, identifies the number of tasks and their associated profiles.

It instantiates other DIMEs based on the information provided, by selecting the resources among the ones available, both the management and the execution layers. In particular, the number of tasks is used to determine the number of needed DIMEs while the information within the profiles becomes instrumental to define 1 the signaling sub-network, 2 the type of relationship between the mediator DIMEs composing the signaling sub-network and the FM of each worker DIME and, finally, 3 the configuration of all the MICEs of each worker DIME to build the most suitable environment for the execution of the workflow.

In this way, the Supervisor is able to create a subnetwork that implements specific workflows that are FCAPS managed both at management layer through the mediators and at execution layer through the FM of each worker DIME. The prototype demonstrates following features: DIME worker fault management which assures when a heartbeat fails from a worker DIME, the orchestrator re-instantiates the worker, re-loads and executes the program 2.

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Simple security check with login authentication before executing regulation commands by each worker. Network-wide auto-scaling, self-repair, performance monitoring and management and distributed workflow execution The details of DIMEs in Linux implementation are discussed in [4]. This paper demonstrates the implementation of parallel signaling channel for service management and demonstrates auto-scaling, self-repair and performance management of Linux processes encapsulated as DIMEs. The figure shows the screen for dynamically reconfiguring FCAPS parameters of each application at run time and the application status.

After the hardware fault, the application with self-repair policy is automatically recovered on a new server where a free DIME was available and the program with no recovery policy associated is not recovered. The details of implementation of Parallax are described in [5, 6]. An orchestrator allows creating services with service regulation and service executable packages and orchestrates the workflow based on policies.

In summary, both DIMEs in Linux and Parallax approaches have demonstrated the feasibility of service management separation from service execution and DIMEs in Multi-Core Server Using a Native Operating System Called Parallax 57 dynamic reconfiguration of service regulation to implement self-repair, auto-scaling, performance management etc. The purpose of this research brief is to propose a new approach different from conventional computing and current cloud and grid computing approaches and to demonstrate its feasibility.

These approaches demonstrate self-repair, auto-scaling and live migration, albeit on a small prototype scale, without the use of Hypervisor or a plethora of management systems. In order to take this research to next level, it requires larger participation from the research community. Only such an effort with an open mind will decide whether this approach has any merit. Given the established and vested interests in existing approaches it is not easy to get attention to new ideas either through academic research or venture capital.

This research brief is an open call for such an effort. Barrett, Beyond the Brain: These features are used to identify some future research directions. In order to take these concepts to practical application in mission critical environments, the DME network architecture based prototypes require validation and acceptance by a larger community. It adds self-monitoring and self-control of each Turing computing node and a parallel signaling enabled network to implement the management of temporal behavior of workflows executed as directed acyclic graphs using a network of managed Turing machines.

The two prototypes demonstrate that the parallel signaling overlay and continuous monitoring and control at specified interval based on business priorities, workload fluctuations and latency constraints enable programming auto-scaling, self-repair, performance optimization and end-to-end transaction management.

The signaling abstractions uniquely differentiate this approach from conventional computing or the grid and cloud strategies. Signaling in the DIME network architecture is as important as it is in cellular organisms to provide resilience [1, 2]. In summary, the DIME network architecture adopts the following key abstractions: Parallel signaling channel for monitoring and control of a distributed network of autonomous computing elements the Turing machines , 2.

Programmable self-managing capabilities at the node and the network level providing a way to create a blueprint for the business workflow managed Turning machine network and 3. A mechanism to monitor and execute FCAPS policies based on business priorities, workload fluctuations and latency constraints. This approach is in contrast to the current approaches [3—10] that use von-Neumann computing model for service management where management and execution of services are serialized both in the node operating system and the network a plethora of resource and service management systems.

The demonstration of live migration of services is accomplished by DIME networks depending on end-to-end service level monitoring and control of distributed transactions as opposed to resource management at each node. The advent of many-core severs with hundreds and even thousands of computing cores with high bandwidth communication among them makes the current generation server, networking and storage equipment and their management systems which have evolved from server-centric and bandwidth limited architectures completely unsuited to use in the next generation computing infrastructure efficiently.

We argue that the recursive network nature of many-core servers with different bandwidths at different levels is ideally suited to exploit the DIME network architecture. The DIME network architecture offers new directions of research to provide next level of scaling, telecom grade trust through end-to-end service FCAPS optimization and reduced complexity in developing, deploying and managing distributed federated software systems executing temporal business workflows. Similarly, the separation of service execution and its management are implemented in the Parallax operating system for the first time at the operating system level.

For example every open , close , read and write operations are part of dynamically reconfigurable operations made possible by parallel signaling channel. This implementation of signaling in the operating system allows the service execution to be dynamically controlled at run time based on FCAPS policies allowing auto-scaling, self-repair, performance monitoring and control, end-to-end transaction security as the two prototypes we have developed demonstrate.

But by introducing parallel control and management of the service workflow, the DIME network architecture improves the scaling, agility and resilience of existing computational workflows both at the node level and at the network level. The signaling based network level control of a service workflow that spans across multiple nodes allows the end-to-end connection level quality of service management independent of the hardware infrastructure management systems.

The only requirement for the hardware infrastructure provider is to assure that the node OS provides the required services for the DIME to load the service regulator and the service execution packages to create and execute the DIME network. The parallax OS is designed to do just that. The network management of DIME services allows different network configurations and management strategies to be dynamically re-configured such as hierarchical scaling using the network composition of sub-networks or peer—peer management systems or client server computing networks.

Each DIME with its autonomy on local resources through FCAPS management and its network awareness through signaling can keep its own history to provide negotiated services to other DIMEs thus enabling a collaborative workflow execution. We identify just a few possible areas of future research that may prove effective: Implementing DNA in current operating systems, as the DIMEs in Linux [12] approach illustrates, provides an immediate path to enhance efficiency of communication between multiple images deployed in a many-core server without any disruption to existing applications.

Current generation operating systems, such as Linux and Windows, can support only few tens of CPUs in a single instance and are inadequate to manage servers that contain hundreds of processors, each with multiple cores. The solutions currently proposed for solving the scalability issue in these systems, i. For example two Linux images communicate with each other using socket communication even though they are neighbors in the same enclosure with shared memory and PCIExpress availability.

The DIME network architecture fills this operating system gap defined as the difference between the number of cores available in an enclosure and the number of cores visible to a single image instance of an OS by dynamically switching the communication behaviors from shared memory or PCIExpress or Socket communication depending on a transaction need. Auto-scaling, performance optimization, end-to-end transaction security and self-repair attributes allow various applications currently running under Linux or Windows to migrate easily to more efficient many-core operating platforms while avoiding a plethora of management systems.

Implementing DNA on virtual servers in current cloud computing infrastructure such as Amazon AWS or Microsoft Azure by encapsulating a process in conventional OS allows intercloud resiliency, efficiency and scaling. In addition, the service management independence from infrastructure management allows a new level of visibility and control to service delivery in these clouds. The service creation and workflow orchestration platforms can be implemented on current generation development environments whereas the run time services deployment and management can be orchestrated in many-core servers with DNA as demonstrated in the prototype.

Future Research Directions 63 5. As hundreds of cores in a single processor enable thousands of cores in a server, the networking infrastructure and associated management software including routing, switching and firewall management will migrate to the server inside from the data center outside. The DIME network architecture with its connection FCAPS management using signaling control will eliminate the need to replicate current network management infrastructure e. The separation of services management from the underlying hardware infrastructure management allows a certain relief from denial of services attacks on the infrastructure.

For example, the signaling allows detection of poor response and immediate response in case of an attack on a particular portion of the infrastructure. Eventually, it is possible to conceive of signaling being incorporated in the many-core processor itself to leverage the DNA in hardware. Conclusion We argue that the DIME network architecture is a next step in the evolution of computing models from von-Neumann serial computing to a network-centric parallel non-von Neumann computing model where each Turing machine is managed and signaling enabled.

Evolution of living organisms has taught us that the difference between survival and extinction is the information processing ability of the organism to: Discover and encapsulate the sequences of stable patterns that have lower entropy, which allow harmony with the environment providing the necessary resources for its survival, 2. Replicate the sequences so that the information in the form of best practices can propagate from the survived to the successor, 3. Execute with precision the sequences to reproduce itself, 4.

Monitor itself and its surroundings in real-time, and 5. Utilize the genetic transactions of repair, recombination and re-arrangement to sustain existing patterns that are useful. The DIME network architecture attempts to implement similar behavior in computing architecture to improve the resiliency, efficiency and scaling of 64 5 Dime Network Architecture-Future Research Directions and Conclusion Fig. This is made possible by two technology advances—the many-core processors with parallelism and performance required to effectively implement the new computing model and the high bandwidth that allows the temporal dynamics of distributed computing to be effectively managed.

By supporting the four genetic transactions of replication, repair, recombination and reconfiguration, the DIME computing model comes close to what von Neumann was searching for in his Hixon lectures [17]. In our dealings with artificial automata, on the other hand, we require an immediate diagnosis. Therefore, we are trying to arrange the automata in such a manner that errors will become as conspicuous as possible, and intervention and correction follow immediately.

The purpose of this research brief is to offer an alternative. Only time will tell if the new approach is useful enough to cross the barriers to adoption in mission critical environments. References 65 References 1. Patterson, The trouble with multi-core. Spectrum, IEEE 47 7 , 28—32, 53 4. A new OS architecture for scalable multicore systems. The case for a scalable operating system for multicores. Berkeley, USA, June 8. An operating system for many cores. San Diego, California, Dec Jin, Single system image. Burks MIT Press, , p. Principles of Distributed Database Systems.

A New Class of Plant Hormones. A New Class of Algorithms. Mechanics of non-holonomic systems a new class of control systems. Designing Management Information Systems. Designing Interfaces for the Distributed Ecosystem. Designing fuzzy logic systems. Designing Relational Database Systems. A Matter of Class. Distributed Autonomous Robotic Systems 7.

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Designing a New Class of Distributed Systems (SpringerBriefs in Electrical and Computer Engineering) Designing a New Class of Distributed Systems (SpringerBriefs in Electrical and Computer Engineering)
Designing a New Class of Distributed Systems (SpringerBriefs in Electrical and Computer Engineering) Designing a New Class of Distributed Systems (SpringerBriefs in Electrical and Computer Engineering)
Designing a New Class of Distributed Systems (SpringerBriefs in Electrical and Computer Engineering) Designing a New Class of Distributed Systems (SpringerBriefs in Electrical and Computer Engineering)
Designing a New Class of Distributed Systems (SpringerBriefs in Electrical and Computer Engineering) Designing a New Class of Distributed Systems (SpringerBriefs in Electrical and Computer Engineering)
Designing a New Class of Distributed Systems (SpringerBriefs in Electrical and Computer Engineering) Designing a New Class of Distributed Systems (SpringerBriefs in Electrical and Computer Engineering)
Designing a New Class of Distributed Systems (SpringerBriefs in Electrical and Computer Engineering) Designing a New Class of Distributed Systems (SpringerBriefs in Electrical and Computer Engineering)
Designing a New Class of Distributed Systems (SpringerBriefs in Electrical and Computer Engineering) Designing a New Class of Distributed Systems (SpringerBriefs in Electrical and Computer Engineering)
Designing a New Class of Distributed Systems (SpringerBriefs in Electrical and Computer Engineering)

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