IEEE 802.15.4-2015 is the third revision of IEEE 802.15.4 Standard for Low-Rate Wireless Networks. The standard presents Time Slotted Channel Hopping (TSCH) Medium Access Control (MAC) protocol, which provides high reliability and low power consumption to various industrial applications. Despite the effectiveness and the importance of the TSCH protocol, the standard leaves out of its scope in defining how the schedule is built and maintained. In this work, we focus on scheduling in IEEE 802.15.4-2015 TSCH networks from the energy efficiency perspective in a centralized manner where the gateway makes frequency allocations and time slot assignments. At first, we derive an energy consumption model of a TSCH node to determine the network lifetime. Afterwards, we formulate the scheduling problem as an energy efficiency maximization problem, which is a nonlinear integer programming. Motivated by the high computational complexity of the problem, we propose a low-complexity Energy Efficient Scheduler (EES) and Vogel's Approximation Method Heuristic Scheduling Algorithm (VAM-HSA). We make a comparison with the Round Robin Scheduler (RRS) and analyse the schedulers in terms of success probability and energy efficiency. Performance evaluation indicates that EES and VAM-HSA perform better in terms of energy efficiency, while at the same time yielding a good throughput

Abstract-One of the main challenges in cloud computing is to increase the availability of computational resources, while minimizing system power consumption and operational expenses. This article introduces a power efficient resource allocation algorithm for tasks in cloud computing data centers. The developed approach is based on genetic algorithms which ensure performance and scalability to millions of tasks. Resource allocation is performed taking into account computational and networking requirements of tasks and optimizes task completion time and data center power consumption. The evaluation results, obtained using a dedicated open source genetic multi-objective framework called jMetal show that the developed approach is able to perform the static allocation of a large number of independent tasks on homogeneous single-core servers within the same data center with a quadratic time complexity.

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In this paper, we present a set of power-aware dynamic allocators for virtual machines (VMs) in cloud data centers (DCs) taking advantage of the software defined networking paradigm. Each VM request is characterized by four parameters: 1) CPU; 2) RAM; 3) disk; and 4) bandwidth. We design the allocators in order to accept as many VM requests as possible, taking into account the power consumption of the network devices. In this paper, we introduce ten different allocation strategies, and compare them with a baseline that consists of using the first available server (first fit). The allocators differ in terms of allocation policy (best fit/worst fit), allocation strategy (single/multi objective optimization), and joint/disjoint selection of IT and network resources. For both joint and disjoint approaches, we evaluate the behavior of all possible pairs policy-strategy, varying the load of the DC and the number of VMs to be allocated. Moreover, the experimental results highlight that joint approaches outperform disjoint ones

The rapidly increasing demand of Cloud services, asking for a flexible and dynamic design of the Cloud, has become a major challenge in DC deployment. Classical Traffic Engineering approaches are no longer enough to deal with the efficient use of IT and network resources in this highly distributed scenario. To cope with this issue, we propose two Fuzzy logic controllers for DC resource allocation based on Mamdani and Sugeno inference processes, that are able to take advantage of simple heuristic rules for efficient virtual machines allocation. To test the effectiveness of the proposed controllers we compare their performance with two variants of Multi-objective allocators as well as a simple Mono-dimensional algorithm. Preliminary simulation tests validate our proposal in terms of number of allocated requests and average server resource utilization.

Time Slotted Channel Hopping (TSCH), defined among the operating modes in IEEE 802.15.4-2015 standard, was established to offer a guaranteed quality of service for deterministic industrial type applications. However, the standard only provides a framework but it does not mandate a specific scheduling mechanism. In this paper, we formulate the NP-hard scheduling problem in terms of maximizing the throughput with deadline constraints and at the same time satisfying interference constraints in TSCH Networks. In the considered TSCH network, a centralized entity typically called gateway, coordinates the assignment of frequencies and timeslots to the nodes. To solve this NP-hard throughput scheduling problem, a Genetic Algorithm (GA) framework was adopted. Simulation results corroborate that our GA-based approach yields very close performance to the optimal solutions and operates with much lower complexity. In addition, the results also confirmed that GA outperforms other popular scheduling algorithms in the literature in terms of throughput maximization as well minimizing violated deadlines

In this paper, we propose a Virtual Machine (VM) allocator for Cloud Computing Data Center (DC). We allocate a set of VMs on servers that are interconnected through a three-tier fat-tree network topology. VMs require four different resources: CPU, memory, disk, and bi-directional network bandwidth forcommunications directed to and coming from the external gateway. Our goal is not to overload computing devices (i.e. allocating more resource than servers' availability) while reducing servers and switches power consumption, in the current proposal, power consumption of each device follows a load-proportional trend. The allocation problem is combinatorial and non-convex, and it is a variant of the multi objective bin packing problem which is NP-Hard. For these reasons, we solve the problem using a particular kind of heuristics called Multi Objective Genetic Algorithm (MOGA) and inspired by the natural process of evolution, MOGA is quite often able to effectively approximate complex problems, such us the one considered. We perform a comparison with a simplified and single-objective formulation of the problem that is solved using CPLEX, while solutions are evaluated using specific quality indicators. The results show how the presented approach solves the allocation problem: MOGA retrieves good quality solutions in less than ten seconds allocating thousands of Vms and obtaining the sameresults as CPLEX

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