An Analysis of many IoT deployments showed that most of them can address the sustainable development goals (SDGs) and the UN’s 2030 agenda. Interestingly, most of these projects concentrate on five SDGs: 1) industry, innovation, infrastructure; 2) smart cities and communities; 3) affordable and clean energy; 4) good health and well-being; and 5) responsible production and consumption. Examples include a remote water-monitoring solution that ensures clean water in regions with an indigenous population and smart lighting initiatives in Chinese cities that halve total power output.

The paper presents a novel approach and algorithm with mathematical formula for obtaining the exact optimal number of task resources for any workload running on Hadoop MapReduce. In the era of Big Data, energy efficiency has become an important issue for the ubiquitous Hadoop MapReduce framework. However, the question of what is the optimal number of tasks required for a job to get the most efficient performance from MapReduce still has no definite answer. Our algorithm for optimal resource provisioning allows users to identify the best trade-off point between performance and energy efficiency on the runtime elbow curve fitted from sampled executions on the target cluster for subsequent behavioral replication. Our verification and comparison show that the currently well-known rules of thumb for calculating the required number of reduce tasks are inaccurate and could lead to significant waste of computing resources and energy with no further improvement in execution time.

Reservation of lightpaths in dynamically switched optical networks facilitates guaranteed bandwidth. However, reservation of bandwidth can often lead to bandwidth fragmentation which significantly reduces system utilization and increases the blocking probability of requests. An interesting approach to mitigating this problem is to induce quasi-flexibility in the user requests. A smart scheduling strategy can then exploit this quasi- flexibility and optimize bandwidth utilization. However, there has to be an incentive for flexibility from the user's perspective as well. In this paper, we explore how the network service provider (NSP) can influence user flexibility by dynamically engineering pricing incentives and by suggesting request realignment to overcome reservation contention. Ultimately, user flexibility will lead to efficient network utilization, reduce the price for the users, and increase the revenue for the NSP.

Advance-reservation is an essential feature of any system in which resources may need to be co-allocated at predetermined times. In this paper, we discuss unconstrained advance reservations, which use flexible time-windows to lower blocking probability and, consequently, increase resource utilization. We claim and show using simulations that the minimum window size, which theoretically brings the blocking probability to zero, in a first-come-first-served advance reservation model without time-slots, equals the waiting time in a queue-based on-demand model. We also show, with simulations, the effect of the window size on the blocking probability and on the resource utilization, for an advance reservation model with time-slots, for different types of arrival and service times. We then compare the blocking probabilities obtained by on-demand reservations, advance reservations, and unconstrained advance reservations with flexibility.

The ability to reserve network bandwidth is a critical factor for the success of high-performance grid applications. Reservation of lightpaths in dynamically switched optical networks facilitates guaranteed bandwidth. However, reservation of bandwidth can often lead to bandwidth fragmentation which significantly reduces system utilization and increases the blocking probability of requests. An interesting approach to mitigating this problem is to induce quasi-flexibility in the user requests. A smart scheduling strategy can then exploit this quasi-flexibility and optimize bandwidth utilization. However, there has to be an incentive for flexibility from the user's perspective as well. In this paper, we explore how the Network Service Provider (NSP) can influence user flexibility by dynamically engineering pricing incentives. Ultimately, user flexibility will lead to efficient network utilization, reduce the price for the users, and increase the revenue for the NSP.

This paper focuses on the scheduling of multiple-wavelength lightpaths and outlines an algorithm to provide increased simultaneous allocations of lightpaths on the lambda-grid. Several approaches for lightpath scheduling have been proposed, and most of them involve some variation of concentrating or balancing wavelengths over the edge-disjoint paths between the source and destination specified in a request. We have used our LRSS, lightpath request scheduling simulator, to show that the efficiency of balancing- and concentrating-based algorithms depends on the characteristics of the lightpaths requested. Based on this assumption, we have developed a dynamically adaptive hybrid algorithm, which combines both balancing and concentrating, employing each of the approaches when appropriate. The efficiency of the new algorithm is assessed by comparing its performance with the performance of the two basic approaches. Our experiments show that the dynamically adaptive hybrid scheduling algorithm has a blocking probability that is always close to the lower one, for a representative set of topologies under a representative set of traffic conditions.

The scheme of advance reservations in dynamically provisioned optical networks is novel, and there are no grid-based applications designed to utilize this scheme. We formally define and analyze this scheme and present a constrained mathematical model for advance reservations. We also introduce FONTS - the Flexible Optical Network Traffic Simulator, a tool for simulating advance reservation, on-demand, and periodic data transfer requests. FONTS is based on a stochastic model and incorporates a variety of variables, which have been identified to accurately model advance reservation requests. FONTS validates the mathematical model and also helps to analyze complex scenarios beyond the scope of this paper.

Advances in Grid technology enable the deployment of data-intensive distributed applications, which require moving terabytes or even petabytes of data between data banks. The current underlying networks cannot provide dedicated links with adequate end-to-end sustained bandwidth to support the requirements of these Grid applications. DWDM-RAM is a novel service-oriented architecture, which harnesses the enormous bandwidth potential of optical networks and demonstrates their on-demand usage on the OMNInet. Preliminary experiments suggest that dynamic optical networks, such as the OMNInet, are the ideal option for transferring such massive amounts of data. DWDM-RAM incorporates an OGSI/OGSA compliant service interface and promotes greater convergence between dynamic optical networks and data intensive Grid computing.

Next generation applications and architectures (for example, Grids) are driving radical changes in the nature of traffic, service models, technology, and cost, creating opportunities for an advanced communications infrastructure to tackle next generation data services. To take advantage of these trends and opportunities, research communities are creating new architectures, such as the Open Grid Service Architecture (OGSA), which are being implemented in new prototype advanced infrastructures. The DWDM-RAM project, funded by DARPA, is actively addressing the challenges of next generation applications. DWDM-RAM is an architecture for data-intensive services enabled by next generation dynamic optical networks. It develops and demonstrates a novel architecture for new data communication services, within the OGSA context, that allows for managing extremely large sets of distributed data. Novel features move network services beyond notions of the network as a managed resource, for example, by including capabilities for dynamic on-demand provisioning and advance scheduling. DWDM-RAM encapsulates optical network resources (Lambdas, lightpaths) into a Grid service and integrates their management within the Open Grid Service Architecture. Migration to emerging standards such as WS-Resource Framework (WS-RF) should be straightforward. In initial applications, DWDM-RAM targets specific data-intensive services such as rapid, massive data transfers used by large scale eScience applications, including: high-energy physics, geophysics, life science, bioinformatics, genomics, medical morphometry, tomography, microscopy imaging, astronomical and astrophysical imaging, complex modeling, and visualization.

Ensembles of distributed, heterogeneous resources, also known as computational grids, have emerged as critical platforms for high-performance and resource-intensive applications. Such platforms provide the potential for applications to aggregate enormous bandwidth, computational power, memory, secondary storage, and other resources during a single execution. However, achieving this performance potential in dynamic, heterogeneous environments is challenging. Recent experience with distributed applications indicates that adaptivity is fundamental to achieving application performance in dynamic grid environments. The AppLeS (Application Level Scheduling) project provides a methodology, application software, and software environments for adaptively scheduling and deploying applications in heterogeneous, multiuser grid environments. We discuss the AppLeS project and outline our findings.

Distributed applications executing on clustered environments typically share resources (computers and network links) with other applications. In such systems, application execution may be retarded by the competition for these shared resources. In this paper, we define a model that calculates the slowdown imposed on applications in time-shared multi-user clusters. Our model focuses on three kinds of slowdown: local slowdown, which synthesizes the effect of contention for CPU in a single workstation; communication slowdown, which synthesizes the effect of contention for the workstations and network links on communication costs; and aggregate slowdown, which determines the effect of contention on a parallel task caused by other applications executing on the entire cluster, i.e., on the nodes used by the parallel application. We verify empirically that this model provides an accurate estimate of application performance for a set of compute-intensive parallel applications on different clusters with a variety of emulated loads.

Most applications share the resources of networked workstations with other applications. Since system load can vary dramatically, allocation strategies that assume that resources have a constant availability and/or capability are unlikely to promote performance-efficient allocations in practice. In order to best allocate application tasks to machines, it is critical to provide a realistic model of the effects of contention on application performance. In this paper, we present a model that provides an estimate of the slowdown imposed by competing load on applications targeted to high-performance clusters and networks of workstations. The model provides a basis for predicting realistic communication and computation costs and is shown to achieve good accuracy for a set of scientific benchmarks commonly found in high-performance applications.

Fast networks have made it possible to coordinate distributed heterogeneous CPU, memory and storage resources to provide a powerful platform for executing high-performance applications. However, the performance of these applications on such systems is highly dependent on the allocation and efficient coordination of application tasks. A key component for a performance-efficient allocation strategy is a predictive model which provides a realistic estimate of application performance under varying resource loads. In this paper, we present a model for predicting the effects of contention on application behavior in heterogeneous systems. In particular, our model calculates the slowdown imposed on communication and computation for non-dedicated two-machine heterogeneous platforms. We describe the model for the Sun/CM2 and Sun/Paragon coupled heterogeneous systems. We present experiments on production systems with emulated contention which show the predicted communication and computation costs to be within 15% on average of the actual costs.

Heterogeneous networks are increasingly being used as platforms for resource-intensive distributed parallel applications. A critical contributor to the performance of such applications is the scheduling of constituent application tasks on the network. Since often the distributed resources cannot be brought under the control of a single global scheduler, the application must be scheduled by the user. To obtain the best performance, the user must take into account both application-specific and dynamic system information in developing a schedule which meets his or her performance criteria. In this paper, we define a set of principles underlying application-level scheduling and describe our work-in-progress building AppLeS (application-level scheduling) agents. We illustrate the application-level scheduling approach with a detailed description and results for a distributed 2D Jacobi application on two production heterogeneous platforms.