Distributed Scheduling Framework Research Articles

Distributed Scheduling for Status Update with Heterogeneous Services under the IRSA Protocol

The peak age-of-information (AoI) performance in irregular repetition slotted ALOHA (IRSA) is revisited in this paper with a particular focus on heterogeneous networks, where types of users may have different numbers of users, active probabilities, and sensitivities to AoI. A distributed scheduling framework that is both conceptually and practically simple is proposed. A series of analyses and approximations are conducted, which lead to a closed-form approximation of the best scheduling policy within the proposed framework. Extensive simulations are provided to confirm that in spite of being extremely low complex, the proposed policy can significantly reduce the weighted average peak AoI, especially for heavily overloaded scenarios.

A distributed scheduling framework based on selfish autonomous agents for federated cloud environments

Today, large-scale distributed Cloud computing infrastructures are often characterized by the fact that each participating organization, like a company in the free economy scenario, when making its resources available, will strive for reducing its own cost and for optimizing its own benefit, regardless of the consequences on the global Cloud’s welfare, in terms of effectiveness and performance losses. The selfish behavior of all these entities (end-users, Cloud providers, underlying enterprises/data centers), competing strategically for resources (distributed storage, processing power, bandwidth), motivates the use of game theory and autonomous agents for effective Multi-User Task Scheduling. Accordingly, we present a novel uncoordinated fully distributed scheduling scheme for federated cloud organizations, based on independent, competing, and self-interested job/task execution agents, driven by optimum social welfare criteria towards a Nash equilibrium solution. The agents’ behavior is also conditioned by marginal costs, to force some kind of implicit coordination between the (often conflicting) objectives of the various entities involved in the cloud. Due to its inherent parallel nature, such schema can provide a significantly better scalability in presence of a large number of tasks and resources involved into the scheduling system.

Distributed throughput-optimal scheduling framework with delay analysis in multi-hop wireless networks

In wireless networks, due to the shared medium, we require sophisticated algorithms to schedule concurrent transmissions that meet the interference constraint where two nodes cannot transmit simultaneously in a guaranteed interference range of each other. For the general ( K -hop) interference model, 1 1 This is the generalization of the interference model where two nodes cannot transmit simultaneously if they are within K-hop neighbors of each other due to the shared wireless medium. especially with K ≥ 2 , the throughput-optimal centralized scheduler needs to solve a NP-Hard problem. It leads to the desire of a distributed, low-complexity but throughput-optimal scheduling algorithm. Inspired by this problem, we generalize a randomized scheduling framework for a K -hop interference model and prove that any scheduling algorithm can achieve the capacity of the system if it satisfies the constraints of this framework. Then, we develop two randomized distributed scheduling algorithms which can be integrated into this framework. In spite of having the constant time-complexity, the first proposed algorithm can lead to the exponential growth of the network delay. The second one is a maximal matching algorithm having better delay performance with polynomial growth of delay in the network size. We also show that the whole time-complexity of this randomized distributed scheduling framework is polynomial in network size with any finite value of K .

A distributed scheduling framework for multi-user diversity gain and quality of service in wireless mesh networks

Wireless multi-hop, mesh networks are being considered as a candidate for wireless backhaul networks which carry data traffic between access networks and the wired Internet. Although existing scheduling algorithms have been adopted for the wireless backhaul networks, they do not yield good performance. In this paper, we study the computational complexity of finding the optimal link schedule for the wireless mesh networks with time-division-duplexing (TDD) operations. We show that the problem of finding the optimal schedule for the mesh networks is #P-complete. Consequently, we propose a heuristic distributed scheduling algorithm and a link utility function for wireless mesh networks. Performance analysis shows that our proposed framework is of linear-time complexity and the proposed utility function makes the long-term throughput allocation converge to the desired level which is proportional to the requirements specified by the routing protocol in use to guarantee quality of service (QoS). Moreover, we show that our framework maintains strong temporal correlation of interference, which is required to ensure proper channel predictions for distributed scheduling and power control. Finally, we compare our scheduling algorithm with a previously proposed tree scheduling and a centralized ideal scheduling. Simulation results reveal that the proposed algorithm achieves high efficiency in terms of network objective as well as the overall network throughput.

Maximizing throughput in wireless networks via gossiping

A major challenge in the design of wireless networks is the need for distributed scheduling algorithms that will efficiently share the common spectrum. Recently, a few distributed algorithms for networks in which a node can converse with at most a single neighbor at a time have been presented. These algorithms guarantee 50% of the maximum possible throughput. We present the first distributed scheduling framework that guarantees maximum throughput . It is based on a combination of a distributed matching algorithm and an algorithm that compares and merges successive matching solutions. The comparison can be done by a deterministic algorithm or by randomized gossip algorithms. In the latter case, the comparison may be inaccurate. Yet, we show that if the matching and gossip algorithms satisfy simple conditions related to their performance and to the inaccuracy of the comparison (respectively), the framework attains the desired throughput.It is shown that the complexities of our algorithms, that achieve nearly 100% throughput, are comparable to those of the algorithms that achieve 50% throughput. Finally, we discuss extensions to general interference models. Even for such models, the framework provides a simple distributed throughput optimal algorithm.

On combinatorial auction and Lagrangean relaxation for distributed resource scheduling

Most existing methods for scheduling are based on centralized or hierarchical decision making using monolithic models. In ihis study, we investigate a new method based on a distributed and locally autonomous decision structure using the notion of combinatorial auction. In combinatorial auction the bidders demand a combination of dependent objects with a single bid. We show that not only can we use this auction mechanism to handle complex resource scheduling problems, but there exist strong links between combinatorial auction and Lagrangean-based decomposition. Exploring some of these properties, we characterize combinatorial auction using auction protocols and payment functions. This study is a first step toward developing a distributed scheduling framework that maintains system-wide performance while accommodating local preferences and objectives. We provide some insights to this framework by demonstrating four different versions of the auction mechanism using job shop scheduling problems.

On combinatorial auction and Lagrangean relaxation for distributed resource scheduling

Most existing methods for scheduling are based on centralized or hierarchical decision making using monolithic models. In this study, we investigate a new method based on a distributed and locally autonomous decision structure using the notion of combinatorial auction. In combinatorial auction the bidders demand a combination of dependent objects with a single bid. We show that not only can we use this auction mechanism to handle complex resource scheduling problems, but there exist strong links between combinatorial auction and Lagrangean-based decomposition. Exploring some of these properties, we characterize combinatorial auction using auction protocols and payment functions. This study is a first step toward developing a distributed scheduling framework that maintains system-wide performance while accommodating local preferences and objectives. We provide some insights to this framework by demonstrating four different versions of the auction mechanism using job shop scheduling problems.