No abstract

No abstract

No abstract

This paper presents a shared-memory self-stabilizing failure detector, asynchronous consensus and replicated state-machine algorithm suite, the components of which can be started in an arbitrary state and converge to act as a virtual state-machine. Self-stabilizing algorithms can cope with transient faults. Transient faults can alter the system state to an arbitrary state and hence, cause a temporary violation of the safety property of the consensus. Started in an arbitrary state, the long lived, memory bounded and selfstabilizing failure detector, asynchronous consensus, and replicated state-machine suite, presented in the paper, recovers to satisfy eventual safety and eventual liveness requirements. Several new techniques and paradigms are introduced. The bounded memory failure detector abstracts away synchronization assumptions using bounded heartbeat counters combined with a balance-unbalance mechanism. The practically infinite paradigm is introduced in the scope of self-stabilization, where an execution of, say, 2 64 sequential steps is regarded as (practically) infinite. Finally, we present the first self-stabilizing wait-free reset mechanism that ensures eventual safety and can be used to implement efficient self-stabilizing timestamps that are of independent interest.

The IT industry is experiencing a disruptive trend for which the entire data center infrastructure is becoming software defined and programmable. IT resources are provisioned and optimized continuously according to a declarative and expressive specification of the workload requirements. The software defined environments facilitate agile IT deployment and responsive data center configurations that enable rapid creation and optimization of value-added services for clients. However, this fundamental shift introduces new challenges to existing data center management solutions. In this paper, we focus on the storage aspect of the IT infrastructure and investigate its unique challenges as well as opportunities in the emerging software defined environments. Current state-of-the-art software defined storage (SDS) solutions are discussed, followed by our novel framework to advance the existing SDS solutions. In addition, we study the interactions among SDS, software defined compute (SDC), and software defined networking (SDN) to demonstrate the necessity of a holistic orchestration and to show that joint optimization can significantly improve the effectiveness and efficiency of the overall software defined environments.

Peer-to-peer systems are prone to faults; Therefore, it is extremely important to design peer-to-peer systems that automatically regain consistency or, in other words, are self-stabilizing. In order to achieve the above, we present a deterministic structure that defines the entire (IP) pointers structure among the machines, for every n machines; i.e., defines the next hop for the insert, delete, and search procedures of the peer-to-peer system. Thus, the consistency of the system is easily defined, monitored, verified, and repaired. We present the HyperTree (distributed) structure, which supports the peer-to-peer procedures while ensuring that the outdegree and the in-degree (the number of outgoing/ incoming pointers) are b log b n where n is the actual number of machines and b is an integer parameter greater than 1. Moreover, the HyperTree ensures that the maximal number of hops involved in each procedure is bounded by log b n. A self-stabilizing peer-to-peer distributed algorithm based on the HyperTree is presented.

Energy efficiency of data centers is gaining importance as energy consumption and carbon footprint awareness are rising. Green Performance Indicators (GPIs) provide measurable means to assess the energy efficiency of a resource or system. Most of the metrics commonly used today measure the energy efficiency potential of a resource, system or application usage, rather than the energy efficiency of the actual usage. In this paper, we argue that the way that the resources and systems are actually used in a given data center configuration is at least as important as the efficiency potential of the raw resources or systems. Hence, for data center energy efficiency, we suggest to both select energy efficient components (as done today), as well as optimize the actual usage of the components and systems in the data center. To achieve the latter, optimization of usage centric GPI metrics should be employed and targeted as a primary green goal. In this paper we identify and present usage centric metrics, which should be monitored and optimized for improving energy efficiency, and hence, reduce the data center carbon footprint.

No abstract