Cooperative computation is a promising approach for localized data processing at the edge, e.g., for Internet of Things(IoT). Cooperative computation advocates that computationally intensive tasks in a device could be divided into sub-tasks, and offloaded to other devices or servers in close proximity. However, exploiting the potential of cooperative computation is challenging mainly due to the heterogeneous and time-varying nature of edge devices. Coded computation, which advocates mixing data in sub-tasks by employing erasure codes and offloading these sub-tasks to other devices for computation, is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a coded cooperative computation framework, which we name Coded Cooperative Computation Protocol (C3P), by taking into account the heterogeneous and time-varying resources of edge devices. C3P dynamically offloads coded sub-tasks to helpers and is adaptive to time-varying resources. We show that (i) task completion delay of C3P is very close to optimal coded cooperative computation solutions, (ii) the efficiency of C3P in terms of resource utilization is higher than 99%, and (iii) C3P improves task completion delay significantly as compared to baselines via both simulations and in a testbed consisting of real Android-based smartphones.

Effective communication among first responders during and in the aftermath of a disaster can affect outcomes dramatically. We seek to build a resilient architecture that allows first responders to communicate even with: 1) damage to infrastructure - civilian and / or specialized communication facilities may be damaged by the disaster, and 2) dynamically formed groups - first responder teams may be formed dynamically in response to a disaster and team member addresses (e.g., phone numbers, network addresses) may not be known to one another. We propose a resilient network architecture that allows efficient communication among first responders during and after a disaster [1]. We seek to support dynamically formed groups for incident response, allowing first responders to securely and conveniently communicate based on roles (names). The architecture supports communication in disasters by 1) building resilience into the framework across all the layers, 2) creating a framework that allows communication by role and identity, rather than addresses, 3) supporting multiple modalities (data, voice) for communication among dynamically formed first responder teams, and 4) providing robust and resilient communication and computing even when facilities are error- and disruption-prone.

Cooperative computation is a promising approach for localized data processing at the edge, e.g., for Internet of Things (IoT). Cooperative computation advocates that computationally intensive tasks in a device could be divided into sub-tasks, and offloaded to other devices or servers in close proximity. However, exploiting the potential of cooperative computation is challenging mainly due to the heterogeneous and time-varying nature of edge devices. Coded computation, which advocates mixing data in sub-tasks by employing erasure codes and offloading these sub-tasks to other devices for computation, is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a coded cooperative computation framework, which we name Coded Cooperative Computation Protocol (C3P), by taking into account the heterogeneous resources of edge devices. C3P dynamically offloads coded sub-tasks to helpers and is adaptive to time-varying resources. We show that (i) task completion delay of C3P is very close to optimal coded cooperative computation solutions, (ii) the efficiency of C3P in terms of resource utilization is higher than 99%, (iii) C3P improves task completion delay significantly as compared to baselines via both simulations and in a test-bed consisting of real Android-based smartphones.

Traditional means for contacting emergency responders depend critically on the availability of the 911 service to request help. Large-scale natural disasters such as hurricanes and earthquakes often result in overloading and sometimes failure of communication facilities. Affected citizens are increasingly using social media to obtain and disseminate information. Social media is not only being used to communicate with first responders but also for people to organically volunteer and seek help from each other, complementing the role of first responders. In this paper, we examine the use of Twitter during two major hurricanes in the U.S. in 2017. We find that there exists a sizable number of people with access to the Internet even in areas where 911 services were down, and they tweet disaster-related information including requests for help. Our analysis indicates that social media can potentially help in disaster management and improve outcomes.

Edge computing is a promising approach for localized data processing for many edge applications and systems including Internet of Things (IoT), where computationally intensive tasks in IoT devices could be divided into sub-tasks and offloaded to other IoT devices, mobile devices, and/or servers at the edge. However, existing solutions on edge computing do not address the full range of challenges, specifically heterogeneity; edge devices are highly heterogeneous and dynamic in nature. In this paper, we develop a predictive edge computing framework with hard deadlines. Our algorithm; PrComp (i) predicts the uncertain dynamics of resources of devices at the edge including energy, computing power, and mobility, and (ii) makes sub-task offloading decisions by taking into account the predicted available resources, as well as the hard deadline constraints of tasks. We evaluate Prcomp on a testbed consisting of real Android-based smartphones, and show that it significantly improves energy consumption of edge devices and task completion delay as compared to baselines.

As the expected emerging Non-Volatile Memory (NVM) technologies, such as 3DXPoint, are in production, there has been a recent push in the big data processing community from storage-centric towards memory-centric. Generally, in large-scale systems, distributed memory management through traditional network with TCP/IP protocol exposes performance bottleneck. Briefly, CPU- centric network involves context switching, memory copy etc. Remote Direct Memory Access (RDMA) technology reveals the tremendous performance advantage over than TCP/IP: Allowing access to remote memory directly bypassing OS kernel. In this paper, we propose Megalloc, a distributed NVM allocator exposes NVMs as a shared address space of a cluster of machines based-on RDMA. Firstly, it makes memory allocation metadata accessed directly by each machine, allocating NVM in coarse-grained way; secondly, adopting fine-grained memory chunk for applications to read or store data; finally, it guarantees high distributed memory allocation performance.

The non-volatile memory (NVM) has the illustrious merits of byte-addressability, fast speed, persistency and low power consumption, which make it attractive to be used as main memory. Commonly, user process dynamically acquires memory through memory allocators. However, traditional memory allocators designed with in-place data writes are not appropriate for non-volatile main memory (NVRAM) due to the limited endurance. For instance, the number of write operations is merely 108 times per PCM cell. In this paper, we quantitatively analyze the wear-oblivious of DRAM-oriented designed allocator-glibc malloc and the inefficiency of wear-conscious allocator-NVMalloc. For example, the average imbalance factor (the maximum/the average) of memory allocation is about 7.5 and 3, respectively. Based on our observations, we propose WAlloc, an efficient wear-aware manual memory allocator designed for NVRAM, decouples metadata and data, uses Less Allocated First Out allocation policy and redirects the data writes. Experimental results show that the wear-leveling of WAlloc outperforms that of NVMalloc about 30% and 60% under random workloads and well-distributed workloads, respectively. In addition, considering the trade-off between space and wear-leveling, WAlloc reduces average data memory writes in 64 bytes block by average 1.5X comparing with malloc with almost 8% extra space overhead.